Coronavirus epidemic in Switzerland: integrating clinical, epidemiological, biological and behaviour with mathematical modelling

Background: In late December 2019, a novel coronavirus named SARS-CoV-2 was identified, with first cases occurring in Wuhan City, Hubei Province, China. A massive spread of the disease (COVID-19) in China was soon followed by increasing numbers of cases in many other countries. In Switzerland, the first case was detected on February 25th. Since then the number of cases has increased rapidly to 3028 as of March 18. To better understand the transmission of the virus in Switzerland, and to integrate effective interventions, it is necessary to integrate international research findings from published literature and pre-print articles, with national surveillance data, and information from the internet and social media.Objectives: The overall aim of this project is to analyze and integrate these different data sources as quickly as possible, thereby creating a novel interdisciplinary surveillance system for SARS-CoV-2/COVID-19 in Switzerland. The system will be flexible so that it can be adapted to future outbreaks.Methods: We will perform several independent sub-projects that also inform each other. Some of the findings will be integrated into a mathematical simulation model of COVID-19 transmission in Switzerland. The first sub-project is a semi-automated systematic review of the scientific literature (including preprint articles) on SARS-CoV-2/COVID-19. We will perform repeated topic modelling (using e.g. Latent Dirichlet Allocation or UMAP algorithms) of all available articles. Each article will be attributed to one of several topics and the process is repeated several times. The aim is to quickly identify articles of various topics of interest (e.g. clinical course of disease, mathematical models of spread of disease, economic consequences, biologic studies on vaccine development and immunologic response, etc), without the need to define exact search terms a-priory. Papers will be made accessible and searchable through a web user interface, as well as an API. The findings will be made publically available, and will inform both the Swiss and the international response to the epidemic. Key parameters for the parameterization of the mathematical model, will be extracted automatically or by hand search from the identified full text articles. As a possible extension we will also analyse social media data (e.g. from Twitter, Facebook and Reddit) in real-time which may help to understand how behaviour of individuals changes as the epidemic evolves. The second subproject concerns the analysis and comparison of existing Swiss surveillance data. Data include i) a sentinel surveillance system of 10 Swiss hospitals (project being finalized; partly funded by the Federal Office of Public Health FOPH; O Keiser is the principle investigator (PI)); ii) Sentinella (Influenza/COVID surveillance by general practitioners; available by FOPH); Grippenet (app to report influenza-like symptoms voluntarily; PI A Flahault). In addition we aim to integrate whole genome sequence data as soon as they become available from the national reference lab for emerging viruses in Geneva (PI I Eckerle). We will compare the findings from the different data sources to each other and to published literature. For example, we will analyse predictors for progression and outcome of the disease. We will use state-of-the art analyses methods including e.g. regression analyses. As third sub-project we will develop a novel mathematical model for Switzerland that includes the progression and transmission of the disease. and that will be directly linked to the surveillance data. The model will be parameterized in real-time using the literature and surveillance data where possible; it will build on our previous expertise with mathematical modelling and include how individuals react to the epidemic.Relevance of study: Combining the mathematical model with the other sub-projects will give us a deeper understanding of the COVID-19 epidemic, and allow us to evaluate the effectiveness of interventions (e.g. by identifying risk factors, by improving the management of hospital beds; by focusing interventions on areas of intense viral circulation, and by detecting more or less virulent strains). Our interdisciplinary research project combines analyses of traditional surveillance data, fundamental research, phylogenetic analyses and mathematical modelling. We bring together epidemiologists/statisticians, modellers, clinicians with expertise in infection control and prevention and virologists with longstanding expertise on Coronaviruses. Our project will provide insight about the course of the disease and the circulation of COVID-19 in Switzerland. Possible interventions will be discussed with the Federal Office of Public Health. All relevant scientific findings from the project will be made available immediately on a dedicated website.